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arxiv: 2510.18245 · v3 · pith:MNKAKQ75new · submitted 2025-10-21 · 💻 cs.LG · cs.AI

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs

Song Bian , Tao Yu , Shivaram Venkataraman , Youngsuk Park This is my paper

Pith reviewed 2026-05-18 05:25 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords scaling lawslarge language modelsmodel architectureinference efficiencygrouped-query attentionChinchilla scaling lawLLM optimizationtraining budget
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The pith

A conditional scaling law that adds architectural details to Chinchilla predicts LLM designs with higher accuracy and faster inference than standard baselines.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that key architectural choices in large language models, including hidden size, the balance of parameters between MLP and attention layers, and the use of grouped-query attention, can be folded into scaling laws to guide the creation of models that are accurate yet cheap to run at inference time. A reader would care because inference now dominates the cost of deploying these models, so any reliable way to pick better designs without extra training compute or data offers immediate practical value. The authors define a conditional scaling law that augments the Chinchilla framework with these architectural terms, pair it with a search procedure, and validate both on more than two hundred models ranging from 80 million to 3 billion parameters trained on 8 billion to 100 billion tokens. The fitted law is shown to forecast strong architectures, and the resulting models exceed open-source baselines in both accuracy and speed under matched training budgets.

Core claim

The central claim is that a conditional scaling law formed by augmenting the Chinchilla framework with information on hidden size, the MLP-to-attention ratio, and grouped-query attention can reliably predict architectural choices that are simultaneously accurate and inference-efficient. This is shown by training more than 200 models across 80M to 3B parameters and 8B to 100B tokens, fitting the law, and using it to select designs that, under the same training budget, reach up to 2.1 percent higher accuracy and 42 percent greater inference throughput than LLaMA-3.2.

What carries the argument

The conditional scaling law that augments the Chinchilla scaling law with terms for hidden size, MLP-to-attention ratio, and grouped-query attention to jointly model loss and inference latency.

If this is right

  • Architectural choices such as hidden size and GQA can be selected systematically using the fitted conditional scaling law rather than exhaustive trial.
  • Models produced this way deliver up to 2.1 percent higher accuracy than LLaMA-3.2 when trained on the same budget.
  • The same models also achieve up to 42 percent higher inference throughput than the baseline.
  • The conditional scaling law provides accurate forecasts of both loss and latency across the studied range of model sizes and data volumes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the low-dimensional augmentation continues to work, the same approach could guide architecture search for models larger than the 3B scale examined here.
  • Treating inference cost as an explicit term inside scaling laws may encourage future scaling studies to optimize for deployment cost from the outset.
  • Analogous conditional laws could be derived for other architectural families or training objectives to extend the efficiency gains.

Load-bearing premise

The effects of hidden size, MLP-to-attention ratio, and GQA on both loss and inference latency can be captured by a low-dimensional additive or multiplicative augmentation to the Chinchilla scaling law without large unmodeled interactions or regime shifts outside the 80M-3B parameter range studied.

What would settle it

Training a new model with the architecture selected by the conditional scaling law and finding that it fails to exceed LLaMA-3.2 in accuracy or inference throughput under the same training budget would falsify the central claim.

Figures

Figures reproduced from arXiv: 2510.18245 by Shivaram Venkataraman, Song Bian, Tao Yu, Youngsuk Park.

Figure 1
Figure 1. Figure 1: Although larger models gener￾ally achieve lower inference throughput than smaller ones, Qwen2.5-1.5B outper￾forms Qwen3-0.6B. Despite having the same number of layers, Qwen2.5-1.5B benefits from a higher hidden size, GQA, and mlp-to-attention ratio. In this work, we fix the number of layers and study the effect of other architectural factors, including GQA, hidden size, and the mlp-to-attention ratio. This… view at source ↗
Figure 2
Figure 2. Figure 2: Inference throughput vs (left) hidden size d = dmodel and (right) mlp-to-attention ratio r = rmlp/attn on the 8B model. Under a fixed parameter budget Nnon-embed, larger hidden sizes and higher mlp-to-attention ratios improve inference throughput for varying batch sizes. Chinchilla addresses the following question to determine optimal allocation: arg min N,D L(N, D) s.t. FLOPs(N, D) = C (2) where C denotes… view at source ↗
Figure 3
Figure 3. Figure 3: Loss vs. hidden size: (Left) 80M model variants; (Center) 145M model variants; (Right) 297M model variants. Across model sizes, the relationship between training loss and dmodel/ √ N ex￾hibits a consistent U-shaped curve when architectural factors such as GQA and the MLP-to-attention ratio are held fixed. The legend denotes the MLP-to-attention ratio r = rmlp/attn for each model. 0 1 2 3 4 5 6 7 mlp-to-att… view at source ↗
Figure 4
Figure 4. Figure 4: Loss vs. MLP-to-attention ratio: (Left) 80M model variants; (Center) 145M model variants; (Right) 297M model variants. Across model sizes, the relationship between training loss and rmlp/attn exhibits a consistent U-shaped curve when architectural factors such as GQA and hidden size are held fixed. The legend denotes the hidden size d = dmodel for each model. separately for x = rmlp/attn and dmodel/ √ Nnon… view at source ↗
Figure 5
Figure 5. Figure 5: Predictive performances of the fitted conditional scaling law on: (left) Task 1: Fit on 80M, evaluate on 145M; (center) Task 2: Fit on 80, 145M, evaluate on 297M; (right) Task 3: Fit on 80, 145, 297M, evaluate on 1B. Orange dots denote fitting data points, and purple crosses indicate the test data points. We compare scaling-law predicted loss with actual pretraining loss of architectures and observed a con… view at source ↗
Figure 6
Figure 6. Figure 6: Results for 1B and 3B models: (left) Panda-1B closely follows the scaling law predictions for minimizing training loss. (center) Inference throughput comparison between LLaMA-3.2-1B and Surefire-1B, showing that Surefire-1B consistently achieves higher efficiency across batch sizes. (right) Inference throughput comparison between LLaMA-3.2-3B and Surefire-3B, demonstrating that Surefire-3B consistently del… view at source ↗
Figure 7
Figure 7. Figure 7: Effect of the Fitting Dataset on Predictive Performance vs (left) Fit on 80, 145, 297M, 1B, evaluate on 3B; (right) Fit on 1B, evaluate on 3B. Orange dots denote fitting data points, and purple crosses indicate the test data points. We compare scaling-law predicted loss with actual pretraining loss of architectures and we observe that fitting the scaling laws with only 1B model data yields lower MSE and hi… view at source ↗
Figure 8
Figure 8. Figure 8: Hidden size on Inference Throughput: (left) 1B model variants; (center) 3B model variants; (right) 8B model variants. Across varying batch sizes and model scales, larger hidden sizes yield higher inference throughput under a fixed parameter budget. The legend indicates the hidden size of the models, where d = dmodel. 2 4 2 5 2 6 2 7 Batch Size 0 1000 2000 3000 4000 5000 6000 Throughput (tokens/s) r=0.45 r=… view at source ↗
Figure 9
Figure 9. Figure 9: MLP-to-Attention ratio on Inference Throughput: (left) 1B model variants; (center) 3B model variants; (right) 8B model variants. Across varying batch sizes and model scales, a larger MLP-to-Attention ratio increases inference throughput under a fixed parameter budget. The legend indicates the MLP-to-Attention ratio of the models, where r = rmlp/attn. 2 4 2 5 2 6 2 7 Batch Size 0 2000 4000 6000 8000 Through… view at source ↗
Figure 10
Figure 10. Figure 10 [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Hidden size on Inference Throughput (Qwen3): (left) Qwen3-0.6B model variants; (center) Qwen3-1.7B model variants; (right) Qwen3-4B model variants. Across varying batch sizes and model scales, larger hidden sizes yield higher inference throughput under a fixed parameter budget. The legend indicates the hidden size of the models, where d = dmodel. All evaluations are performed using the vLLM framework Kwon… view at source ↗
Figure 12
Figure 12. Figure 12: MLP-to-Attention ratio on Inference Throughput (Qwen3): (left) Qwen3-0.6B model variants; (center) Qwen3-1.7B model variants; (right) Qwen3-4B model variants. Across varying batch sizes and model scales, a larger MLP-to-Attention ratio increases inference throughput under a fixed parameter budget. The legend indicates the MLP-to-Attention ratio of the models, where r = rmlp/attn. All evaluations are perfo… view at source ↗
Figure 13
Figure 13. Figure 13 [PITH_FULL_IMAGE:figures/full_fig_p022_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Loss vs. GQA: (left) 80M model variants; (center) 145M model variants; (right) 297M model variants. Across different model sizes, the relationship between training loss and GQA varies substantially when hidden size and the mlp-to-attention ratio are fixed. The legend denotes the hidden size of each trained model. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: (left) and [PITH_FULL_IMAGE:figures/full_fig_p024_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Joint and non-separable calibrations: (left) use multiplicative calibrations; (right) use joint and non-separable calibrations. We observe that joint and non-separable calibrations yield higher MSE and lower Spearman scores than multiplicative calibrations, indicating inferior perfor￾mance. Dots denote the data points used for fitting, while crosses indicate the test data points. 24 [PITH_FULL_IMAGE:figu… view at source ↗
Figure 17
Figure 17. Figure 17: Active-Experts-to-Attn on Inference Throughput: (left) 3B-A1.1B model variants; (center) 5.3B-A1.7B model variants; (right) 8.3B-A1.5B model variants. We study the effect of the Active-Experts-to-Attention ratio on inference throughput by fixing the total number of active parameters, setting GQA to 4, and using a batch size of 2048 to reduce MoE inference variance in this figure. All evaluations are perfo… view at source ↗
read the original abstract

Scaling the number of parameters and the size of training data has proven to be an effective strategy for improving large language model (LLM) performance. Yet, as these models grow increasingly powerful and widely deployed, the cost of inference has become a pressing concern. Despite its importance, the trade-off between model accuracy and inference efficiency remains underexplored. In this work, we examine how key architectural factors, hidden size, the allocation of parameters between MLP and attention (mlp-to-attention ratio), and grouped-query attention (GQA), influence both inference cost and accuracy. We introduce a conditional scaling law that augments the Chinchilla framework with architectural information, along with a search framework for identifying architectures that are simultaneously inference-efficient and accurate. To validate our approach, we train more than 200 models spanning 80M to 3B parameters and 8B to 100B training tokens, and fit the proposed conditional scaling law. Our results show that the conditional scaling law reliably predicts optimal architectural choices and that the resulting models outperform existing open-source baselines. Under the same training budget, optimized architectures achieve up to 2.1% higher accuracy and 42% greater inference throughput compared to LLaMA-3.2.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes a conditional scaling law that augments the Chinchilla framework with architectural factors (hidden size, MLP-to-attention ratio, and GQA) to jointly model cross-entropy loss and inference latency. A search framework is introduced to identify architectures that optimize accuracy under inference constraints. The approach is validated by training over 200 models spanning 80M–3B parameters and 8B–100B tokens; the authors claim that architectures selected via the fitted law outperform LLaMA-3.2 by up to 2.1% accuracy and 42% inference throughput under equivalent training budgets.

Significance. If the conditional law generalizes and the reported gains hold under independent validation, the work supplies a practical, data-driven method for co-optimizing model architecture and inference efficiency within the Chinchilla scaling regime. The scale of the experimental campaign—more than 200 models across a useful parameter and token range—provides concrete empirical grounding that strengthens the contribution relative to purely theoretical or small-scale studies.

major comments (3)
  1. [Experiments] Experiments section: the conditional scaling law is fitted directly to the full set of >200 experimental runs, after which the same fitted surface is used to select the “optimal” architectures whose performance is then reported. This circularity means the 2.1% accuracy and 42% throughput claims are not supported by held-out validation or independent test runs; a cross-validation split or separate confirmation experiments would be required to substantiate the predictive reliability.
  2. [Conditional Scaling Law] Section describing the conditional scaling law: the augmentation to the Chinchilla law is presented without an explicit functional form (additive, multiplicative, or with interaction terms) or any analysis of whether optimal MLP-to-attention ratio or GQA group size varies with scale. If unmodeled higher-order interactions or regime shifts exist outside the 80M–3B / 8B–100B token window, the search framework will systematically select architectures whose predicted gains do not materialize, directly undermining the central claim.
  3. [Inference Evaluation] Inference evaluation subsection: no description is given of the hardware, batch size, sequence length, or measurement protocol used to obtain the latency/throughput numbers that underpin the 42% improvement claim. Without these details it is impossible to assess whether the reported throughput advantage is reproducible or sensitive to implementation choices.
minor comments (2)
  1. [Abstract] Abstract and results: the fit of the conditional law is reported without error bars, R² values, or residual analysis, making it difficult to judge how well the low-dimensional augmentation actually captures the observed data.
  2. [Results] Results tables: additional baselines beyond LLaMA-3.2 (e.g., recent efficient variants such as Mistral or Phi-3 derivatives) would help contextualize whether the gains are specific to the proposed search or more generally available.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive comments on our manuscript. We address each major comment below and indicate the revisions we will make to improve the paper.

read point-by-point responses

  1. Referee: [Experiments] Experiments section: the conditional scaling law is fitted directly to the full set of >200 experimental runs, after which the same fitted surface is used to select the “optimal” architectures whose performance is then reported. This circularity means the 2.1% accuracy and 42% throughput claims are not supported by held-out validation or independent test runs; a cross-validation split or separate confirmation experiments would be required to substantiate the predictive reliability.

    Authors: We appreciate this important point about potential circularity in our evaluation. While the scaling law was fitted on the full set of experiments to maximize data utilization for the fit, we recognize that this does not provide an independent test of the law's predictive power for architecture selection. To address this, we will include a cross-validation analysis in the revised manuscript, where the law is fitted on a random subset of 80% of the models and used to predict performance on the held-out 20%. Additionally, we will report results from training a small number of confirmation models selected by the law but not included in the original fitting process. revision: yes

  2. Referee: [Conditional Scaling Law] Section describing the conditional scaling law: the augmentation to the Chinchilla law is presented without an explicit functional form (additive, multiplicative, or with interaction terms) or any analysis of whether optimal MLP-to-attention ratio or GQA group size varies with scale. If unmodeled higher-order interactions or regime shifts exist outside the 80M–3B / 8B–100B token window, the search framework will systematically select architectures whose predicted gains do not materialize, directly undermining the central claim.

    Authors: We agree that the functional form should be made explicit. The conditional scaling law augments the Chinchilla loss with multiplicative factors for each architectural parameter: L(N, D, h, r, g) = E + A/N^α + B/D^β * f(h, r, g), where f incorporates the hidden size h, MLP-to-attention ratio r, and GQA group size g. We will add the full equation and a subsection analyzing how the optimal r and g change across different scales within our experimental range. Regarding extrapolation beyond the studied regime, we will add a discussion of the limitations and suggest that the law is intended for the 80M-3B parameter range. revision: yes

  3. Referee: [Inference Evaluation] Inference evaluation subsection: no description is given of the hardware, batch size, sequence length, or measurement protocol used to obtain the latency/throughput numbers that underpin the 42% improvement claim. Without these details it is impossible to assess whether the reported throughput advantage is reproducible or sensitive to implementation choices.

    Authors: We apologize for the omission of these critical details. In the revised manuscript, we will add a dedicated paragraph in the Inference Evaluation subsection specifying the hardware (NVIDIA H100 GPUs), batch size (1 for latency, 32 for throughput), sequence length (2048 tokens), and the measurement protocol (using PyTorch with CUDA events for timing, averaged over 100 runs after warmup). This will allow readers to reproduce and evaluate the sensitivity of the 42% throughput improvement. revision: yes

Circularity Check

1 steps flagged

Conditional scaling law 'predictions' of optimal architectures reduce to optimization over the fitted surface from the same 200-model runs

specific steps
  1. fitted input called prediction [Abstract]
    "we introduce a conditional scaling law that augments the Chinchilla framework with architectural information, along with a search framework for identifying architectures that are simultaneously inference-efficient and accurate. To validate our approach, we train more than 200 models spanning 80M to 3B parameters and 8B to 100B training tokens, and fit the proposed conditional scaling law. Our results show that the conditional scaling law reliably predicts optimal architectural choices and that the resulting models outperform existing open-source baselines."

    The law is fitted to the exact experimental runs whose architectural variants are later declared 'optimal' by the search framework. Selecting the architecture that minimizes the fitted conditional scaling law is tautological; the 'prediction' of optimality is the direct output of the fit rather than an independent forecast or derivation.

full rationale

The paper fits its conditional scaling law directly to loss and latency measurements from the >200 models spanning 80M-3B parameters. It then uses this fitted surface, via a search framework, to identify 'optimal' architectural choices (hidden size, MLP-to-attention ratio, GQA). The claim that the law 'reliably predicts optimal architectural choices' therefore reduces to selecting the argmin of the fitted function rather than an out-of-sample derivation or held-out validation. The subsequent training of those selected architectures and reported gains (2.1% accuracy, 42% throughput) inherit this dependence; no independent first-principles derivation or external benchmark is shown to break the loop.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that a modest number of architectural scalars can be inserted into the Chinchilla functional form and that the resulting surface will generalize to new model sizes and token budgets; the free parameters are the fitted coefficients of that augmented law.

free parameters (1)
  • coefficients of the conditional scaling law
    The parameters that multiply or add the architectural variables (hidden size, mlp-to-attention ratio, GQA) to the base Chinchilla loss equation are fitted to the 200+ training runs.
axioms (1)
  • domain assumption Architectural factors can be treated as continuous variables whose effects on loss and latency are smooth and low-order within the studied regime.
    Invoked when the authors augment the Chinchilla framework rather than treating architecture as a discrete search space.

pith-pipeline@v0.9.0 · 5756 in / 1520 out tokens · 65717 ms · 2026-05-18T05:25:53.853034+00:00 · methodology

discussion (0)

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Reference graph

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